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Analysis on the Juris Master Education Characteristics in Local Engineering Colleges

2012· article· en· W1793161498 on OpenAlexvenueno aff
LI Yan-ping, Liu Ji-sheng

Bibliographic record

VenueHigher education of social science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsSubject (documents)Investment (military)Mode (computer interface)Capital investmentKey (lock)Teaching staffCapital (architecture)Political scienceAtmosphere (unit)ManagementEngineering managementSociologyMathematics educationEngineering ethicsEngineeringBusinessComputer sciencePedagogyLawPsychologyEconomicsLibrary scienceFinanceHistory

Abstract

fetched live from OpenAlex

Local engineering colleges assimilate and learn theJM’s education platform from the traditional politicallaw schools or comprehensive colleges in many aspects.Hence, this has led to these universities resembling eachother in the size of the subject development orientation,education mode and so forth. However, since localengineering colleges are different from these politicallaw schools and comprehensive colleges in the respectof teaching staff construction, academic atmosphere,resources and capital investment, it is safe to say that localengineering universities will not follow suit. With its richengineering resources, it is highly advisable that exploringa new mode of legal master development is the right wayout for local engineering colleges. Key words: Local engineering; Juris master;Characteristic training

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.333
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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